Foundations Machine Learning
bloomberg.github.io
Foundations Machine Learning
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Re: Foundations Machine Learning
#2The title of this page feels like a reference to the excellent Mohri et al. Foundations of Statistical Machine Learning. I’d recommend both it and Shai Shalev-Schwartz for VC theory/Rademacher complexity sorts of statistical ML.
However, I’m not crazy about this summary. It’s less foundational than it is a survey. I probably dislike the format and formatting more than anything, but I would not recommend this over other resources.
Re: Foundations Machine Learning
#3The title of this page feels like a reference to the excellent Mohri et al. Foundations of Statistical Machine Learning. I’d recommend both it and Shai Shalev-Schwartz for VC theory/Rademacher complexity sorts of statistical ML. However, I’m not crazy about this summary. It’s less foundational than it is a survey. I probably dislike the format and formatting more than anything, but I would not recommend this over oth…
Yeah, this space feels pretty saturated already, and people seem to use the same topics / presentation / ordering over and over. What a ton of duplicate effort!
It'd be nice to see a course try to put its own spin on "machine learning" and 1) present standard topics in an unusual way and 2) include topics that intro ML students might not normally see.
Re: Foundations Machine Learning
#4The title of this page feels like a reference to the excellent Mohri et al. Foundations of Statistical Machine Learning. I’d recommend both it and Shai Shalev-Schwartz for VC theory/Rademacher complexity sorts of statistical ML. However, I’m not crazy about this summary. It’s less foundational than it is a survey. I probably dislike the format and formatting more than anything, but I would not recommend this over oth…
Yeah, this space feels pretty saturated already, and people seem to use the same topics / presentation / ordering over and over. What a ton of duplicate effort! It'd be nice to see a course try to put its own spin on "machine learning" and 1) present standard topics in an unusual way and 2) include topics that intro ML students might not normally see.
Is that because they are all online? After all, how many comm 101 courses are taught every year?
Re: Foundations Machine Learning
#5If you know basic undergrad probability/statistics/linear algebra (matrices, vectors, eigenvalues) you can do CMU's graduate course in ML that is intended to prepare PhD students to understand research papers in the field
(Includes recorded lectures) https://sites.google.com/site/10715advancedmlintro2017f/lect...
CMU also has an 'Applied Machine Learning' undergrad course that is paywalled fully unfortunately, but they use the text: Witten, I. H. & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, second edition
Re: Foundations Machine Learning
#6I am new in machine learning and have difficulty to understand the equation. I finish 2 courses from Andrew Machine Learning Class from Coursera, I understand the flow, concept and knew how to write those equations/algorithm but it always bugs me that I don't understand those equations. Do you guys have any suggestion that where should I start for learning those equations/maths online?
Re: Foundations Machine Learning
#7I am new in machine learning and have difficulty to understand the equation. I finish 2 courses from Andrew Machine Learning Class from Coursera, I understand the flow, concept and knew how to write those equations/algorithm but it always bugs me that I don't understand those equations. Do you guys have any suggestion that where should I start for learning those equations/maths online?
What is your math experience/background? In any case, for the average case, this course is probably the best for those who want a slightly deeper understanding of machine learning than offered by Andrew Ng's courses: https://work.caltech.edu/lectures.html
Re: Foundations Machine Learning
#8I am new in machine learning and have difficulty to understand the equation. I finish 2 courses from Andrew Machine Learning Class from Coursera, I understand the flow, concept and knew how to write those equations/algorithm but it always bugs me that I don't understand those equations. Do you guys have any suggestion that where should I start for learning those equations/maths online?
If you want to keep up with the maths, the main prerequisites for more rigorous ML is typically undergrad-level calculus, linear algebra, and bit of basic probability and statistics.
For calculus, google "MIT 18.01", "MIT 18.02", (and "MIT 18.03" if you like), which are all freely available on youtube. You should be comfortable with single-variable calculus, and at least familiar with multi-variable techniques.
For linear algebra, try "MIT 18.06", which is Gilbert Strang's MIT course. Or try 3blue1brown's "The essence of linear algebra" series, which is the best explanation I've ever seen of many concepts, but it is shorter and less in-depth than a full course.
For basic statistics, try Khan Academy's "AP Statistics" sequence.
Re: Foundations Machine Learning
#9I am new in machine learning and have difficulty to understand the equation. I finish 2 courses from Andrew Machine Learning Class from Coursera, I understand the flow, concept and knew how to write those equations/algorithm but it always bugs me that I don't understand those equations. Do you guys have any suggestion that where should I start for learning those equations/maths online?
Depends what your math level is. Start with https://www.expii.com/ which is Po-Shen Loh's site and see where you are. If you can do the algebra and basic calculus problem sets there you could also try these specific ML background books https://sites.google.com/site/10715advancedmlintro2017f/read...
Expii is a great resource because it determines from your success solving problems if you need more practice to get a concept
Re: Foundations Machine Learning
#10The title of this page feels like a reference to the excellent Mohri et al. Foundations of Statistical Machine Learning. I’d recommend both it and Shai Shalev-Schwartz for VC theory/Rademacher complexity sorts of statistical ML. However, I’m not crazy about this summary. It’s less foundational than it is a survey. I probably dislike the format and formatting more than anything, but I would not recommend this over oth…
Yeah, this space feels pretty saturated already, and people seem to use the same topics / presentation / ordering over and over. What a ton of duplicate effort! It'd be nice to see a course try to put its own spin on "machine learning" and 1) present standard topics in an unusual way and 2) include topics that intro ML students might not normally see.
Take a peek at our class [0]! We present a bunch of topics not covered in undergraduate courses (at least to our knowledge), such as proximal gradient descent, random features, etc., without referencing any probability. The course is aimed at mostly Sophomores/Juniors in any STEM field with the only prerequisite being the introductory linear algebra course EE103. [1] All of the material (minus solutions) is available online for both courses.
I'm probably highly biased, but I'd like to say this is a fairly fresh take which departs heavily from the usual CS229 (Ng's course) presentation style and order since it's meant for a completely different audience (and was, to be fair, written this past quarter, unlike 229 which was written perhaps 15 years ago).
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